{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trend Following Strategy\n",
    "\n",
    "This notebook answers question 3.4 form the text book Advances in Financial Machine Learning.\n",
    "\n",
    "3.4 Develop a trend-following strategy based on a popular technical analysis statistic (e.g., crossing moving averages). For each observation, the model suggests a side, but not a size of the bet.\n",
    "\n",
    "* (a) Derive meta-labels for ptSl = [1, 2] and t1 where numDays = 1. Use as trgt the daily standard deviation as computed by Snippet 3.1.\n",
    "* (b) Train a random forest to decide whether to trade or not. Note: The decision is whether to trade or not, {0,1}, since the underlying model (the crossing moving average) has decided the side, {-1, 1}.\n",
    "\n",
    "I took some liberties by extending the features to which I use to build the meta model. I also add some performance metrics at the end. \n",
    "\n",
    "In conclusion: Meta Labeling works, SMA strategies suck."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Hudson and Thames MlFinLab package\n",
    "import mlfinlab as ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/pyfolio/pos.py:28: UserWarning: Module \"zipline.assets\" not found; mutltipliers will not be applied to position notionals.\n",
      "  ' to position notionals.'\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyfolio as pf\n",
    "import timeit\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.utils import resample\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in Data\n",
    "We are using the dollar bars based off of the high quality HFT data we purchased. There is a sample of bars available in this branch as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data\n",
    "data = pd.read_csv('official_data/dollar_bars.csv')\n",
    "data.index = pd.to_datetime(data['date_time'])\n",
    "data = data.drop('date_time', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data['2011-09-01':]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Fit a Primary Model: Trend Following\n",
    "Based on the simple moving average cross-over strategy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute moving averages\n",
    "fast_window = 20\n",
    "slow_window = 50\n",
    "\n",
    "data['fast_mavg'] = data['close'].rolling(window=fast_window, min_periods=fast_window, center=False).mean()\n",
    "data['slow_mavg'] = data['close'].rolling(window=slow_window, min_periods=slow_window, center=False).mean()\n",
    "data.head()\n",
    "\n",
    "# Compute sides\n",
    "data['side'] = np.nan\n",
    "\n",
    "long_signals = data['fast_mavg'] >= data['slow_mavg'] \n",
    "short_signals = data['fast_mavg'] < data['slow_mavg'] \n",
    "data.loc[long_signals, 'side'] = 1\n",
    "data.loc[short_signals, 'side'] = -1\n",
    "\n",
    "# Remove Look ahead biase by lagging the signal\n",
    "data['side'] = data['side'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the raw data\n",
    "raw_data = data.copy()\n",
    "\n",
    "# Drop the NaN values from our data set\n",
    "data.dropna(axis=0, how='any', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1.0    45136\n",
       "-1.0    38762\n",
       "Name: side, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['side'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter Events: CUSUM Filter\n",
    "Predict what will happen when a CUSUM event is triggered. Use the signal from the MAvg Strategy to determine the side of the bet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute daily volatility\n",
    "daily_vol = ml.util.get_daily_vol(close=data['close'], lookback=50)\n",
    "\n",
    "# Apply Symmetric CUSUM Filter and get timestamps for events\n",
    "# Note: Only the CUSUM filter needs a point estimate for volatility\n",
    "cusum_events = ml.filters.cusum_filter(data['close'], threshold=daily_vol['2011-09-01':'2018-01-01'].mean()*0.5)\n",
    "\n",
    "# Compute vertical barrier\n",
    "vertical_barriers = ml.labeling.add_vertical_barrier(t_events=cusum_events, close=data['close'], num_days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/mlfinlab/labeling/labeling.py:117: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  target = target.loc[t_events]\n",
      "2019-04-18 16:32:47.194051 100.0% apply_pt_sl_on_t1 done after 0.11 minutes. Remaining 0.0 minutes.\n"
     ]
    }
   ],
   "source": [
    "pt_sl = [1, 2]\n",
    "min_ret = 0.005\n",
    "triple_barrier_events = ml.labeling.get_events(close=data['close'],\n",
    "                                               t_events=cusum_events,\n",
    "                                               pt_sl=pt_sl,\n",
    "                                               target=daily_vol,\n",
    "                                               min_ret=min_ret,\n",
    "                                               num_threads=3,\n",
    "                                               vertical_barrier_times=vertical_barriers,\n",
    "                                               side_prediction=data['side'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0    4395\n",
       " 1.0    4023\n",
       "Name: side, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = ml.labeling.get_bins(triple_barrier_events, data['close'])\n",
    "labels.side.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Results of Primary Model:\n",
    "What is the accuracy of predictions from the primary model (i.e., if the sec- ondary model does not filter the bets)? What are the precision, recall, and F1-scores?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00      4946\n",
      "           1       0.41      1.00      0.58      3472\n",
      "\n",
      "   micro avg       0.41      0.41      0.41      8418\n",
      "   macro avg       0.21      0.50      0.29      8418\n",
      "weighted avg       0.17      0.41      0.24      8418\n",
      "\n",
      "Confusion Matrix\n",
      "[[   0 4946]\n",
      " [   0 3472]]\n",
      "\n",
      "Accuracy\n",
      "0.4124495129484438\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "primary_forecast = pd.DataFrame(labels['bin'])\n",
    "primary_forecast['pred'] = 1\n",
    "primary_forecast.columns = ['actual', 'pred']\n",
    "\n",
    "# Performance Metrics\n",
    "actual = primary_forecast['actual']\n",
    "pred = primary_forecast['pred']\n",
    "print(classification_report(y_true=actual, y_pred=pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(actual, pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(actual, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A few takeaways**\n",
    "* There is an imbalance in the classes - more are classified as \"no trade\"\n",
    "* Meta-labeling says that there are many false-positives  \n",
    "* the sklearn's confusion matrix is [[TN, FP][FN, TP]] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Fit a Meta Model\n",
    "Train a random forest to decide whether to trade or not (i.e 1 or 0 respectively) since the earlier model has decided the side (-1 or 1)\n",
    "\n",
    "Create the following features: \n",
    "* Volatility\n",
    "* Serial Correlation\n",
    "* The returns at the different lags from the serial correlation\n",
    "* The sides from the SMavg Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>cum_vol</th>\n",
       "      <th>cum_dollar</th>\n",
       "      <th>cum_ticks</th>\n",
       "      <th>fast_mavg</th>\n",
       "      <th>slow_mavg</th>\n",
       "      <th>side</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-09-01 06:08:14.750</th>\n",
       "      <td>1217.50</td>\n",
       "      <td>1224.50</td>\n",
       "      <td>1216.5</td>\n",
       "      <td>1220.00</td>\n",
       "      <td>57388</td>\n",
       "      <td>70028257.0</td>\n",
       "      <td>19018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01 07:48:09.398</th>\n",
       "      <td>1220.00</td>\n",
       "      <td>1224.50</td>\n",
       "      <td>1215.5</td>\n",
       "      <td>1215.75</td>\n",
       "      <td>57394</td>\n",
       "      <td>70005670.5</td>\n",
       "      <td>20160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01 09:13:14.311</th>\n",
       "      <td>1215.75</td>\n",
       "      <td>1216.50</td>\n",
       "      <td>1209.5</td>\n",
       "      <td>1210.00</td>\n",
       "      <td>57691</td>\n",
       "      <td>70000560.5</td>\n",
       "      <td>21107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01 10:51:49.085</th>\n",
       "      <td>1210.00</td>\n",
       "      <td>1213.50</td>\n",
       "      <td>1208.5</td>\n",
       "      <td>1212.50</td>\n",
       "      <td>57801</td>\n",
       "      <td>70003237.5</td>\n",
       "      <td>17728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01 12:23:59.756</th>\n",
       "      <td>1212.50</td>\n",
       "      <td>1216.75</td>\n",
       "      <td>1210.5</td>\n",
       "      <td>1215.75</td>\n",
       "      <td>57656</td>\n",
       "      <td>70000722.0</td>\n",
       "      <td>19398</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            open     high     low    close  cum_vol  \\\n",
       "date_time                                                             \n",
       "2011-09-01 06:08:14.750  1217.50  1224.50  1216.5  1220.00    57388   \n",
       "2011-09-01 07:48:09.398  1220.00  1224.50  1215.5  1215.75    57394   \n",
       "2011-09-01 09:13:14.311  1215.75  1216.50  1209.5  1210.00    57691   \n",
       "2011-09-01 10:51:49.085  1210.00  1213.50  1208.5  1212.50    57801   \n",
       "2011-09-01 12:23:59.756  1212.50  1216.75  1210.5  1215.75    57656   \n",
       "\n",
       "                         cum_dollar  cum_ticks  fast_mavg  slow_mavg  side  \n",
       "date_time                                                                   \n",
       "2011-09-01 06:08:14.750  70028257.0      19018        NaN        NaN   NaN  \n",
       "2011-09-01 07:48:09.398  70005670.5      20160        NaN        NaN   NaN  \n",
       "2011-09-01 09:13:14.311  70000560.5      21107        NaN        NaN   NaN  \n",
       "2011-09-01 10:51:49.085  70003237.5      17728        NaN        NaN   NaN  \n",
       "2011-09-01 12:23:59.756  70000722.0      19398        NaN        NaN   NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log Returns\n",
    "raw_data['log_ret'] = np.log(raw_data['close']).diff()\n",
    "\n",
    "# Momentum\n",
    "raw_data['mom1'] = raw_data['close'].pct_change(periods=1)\n",
    "raw_data['mom2'] = raw_data['close'].pct_change(periods=2)\n",
    "raw_data['mom3'] = raw_data['close'].pct_change(periods=3)\n",
    "raw_data['mom4'] = raw_data['close'].pct_change(periods=4)\n",
    "raw_data['mom5'] = raw_data['close'].pct_change(periods=5)\n",
    "\n",
    "# Volatility\n",
    "raw_data['volatility_50'] = raw_data['log_ret'].rolling(window=50, min_periods=50, center=False).std()\n",
    "raw_data['volatility_31'] = raw_data['log_ret'].rolling(window=31, min_periods=31, center=False).std()\n",
    "raw_data['volatility_15'] = raw_data['log_ret'].rolling(window=15, min_periods=15, center=False).std()\n",
    "\n",
    "# Serial Correlation (Takes about 4 minutes)\n",
    "window_autocorr = 50\n",
    "\n",
    "raw_data['autocorr_1'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=1), raw=False)\n",
    "raw_data['autocorr_2'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=2), raw=False)\n",
    "raw_data['autocorr_3'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=3), raw=False)\n",
    "raw_data['autocorr_4'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=4), raw=False)\n",
    "raw_data['autocorr_5'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=5), raw=False)\n",
    "\n",
    "# Get the various log -t returns\n",
    "raw_data['log_t1'] = raw_data['log_ret'].shift(1)\n",
    "raw_data['log_t2'] = raw_data['log_ret'].shift(2)\n",
    "raw_data['log_t3'] = raw_data['log_ret'].shift(3)\n",
    "raw_data['log_t4'] = raw_data['log_ret'].shift(4)\n",
    "raw_data['log_t5'] = raw_data['log_ret'].shift(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re compute sides\n",
    "raw_data['side'] = np.nan\n",
    "\n",
    "long_signals = raw_data['fast_mavg'] >= raw_data['slow_mavg']\n",
    "short_signals = raw_data['fast_mavg'] < raw_data['slow_mavg']\n",
    "\n",
    "raw_data.loc[long_signals, 'side'] = 1\n",
    "raw_data.loc[short_signals, 'side'] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove look ahead bias\n",
    "raw_data = raw_data.shift(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now get the data at the specified events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get features at event dates\n",
    "X = raw_data.loc[labels.index, :]\n",
    "\n",
    "# Drop unwanted columns\n",
    "X.drop(['open', 'high', 'low', 'close', 'cum_vol', 'cum_dollar', 'cum_ticks','fast_mavg', 'slow_mavg',], axis=1, inplace=True)\n",
    "\n",
    "y = labels['bin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4946\n",
       "1    3472\n",
       "Name: bin, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balance classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training, validation and test sets\n",
    "X_training_validation = X['2011-09-01':'2018-01-01']\n",
    "y_training_validation = y['2011-09-01':'2018-01-01']\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(X_training_validation, y_training_validation, test_size=0.15, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3261\n",
       "1    2111\n",
       "Name: bin, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.concat([y_train, X_train], axis=1, join='inner')\n",
    "train_df['bin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3261\n",
       "0    3261\n",
       "Name: bin, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Upsample the training data to have a 50 - 50 split\n",
    "# https://elitedatascience.com/imbalanced-classes\n",
    "majority = train_df[train_df['bin'] == 0]\n",
    "minority = train_df[train_df['bin'] == 1]\n",
    "\n",
    "new_minority = resample(minority, \n",
    "                   replace=True,     # sample with replacement\n",
    "                   n_samples=majority.shape[0],    # to match majority class\n",
    "                   random_state=42)\n",
    "\n",
    "train_df = pd.concat([majority, new_minority])\n",
    "train_df = shuffle(train_df, random_state=42)\n",
    "\n",
    "train_df['bin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create training data\n",
    "y_train = train_df['bin']\n",
    "X_train= train_df.loc[:, train_df.columns != 'bin']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'max_depth':[2, 3, 4, 5, 7],\n",
    "              'n_estimators':[1, 10, 25, 50, 100, 256, 512],\n",
    "              'random_state':[42]}\n",
    "    \n",
    "def perform_grid_search(X_data, y_data):\n",
    "    rf = RandomForestClassifier(criterion='entropy')\n",
    "    \n",
    "    clf = GridSearchCV(rf, parameters, cv=4, scoring='roc_auc', n_jobs=3)\n",
    "    \n",
    "    clf.fit(X_data, y_data)\n",
    "    \n",
    "    print(clf.cv_results_['mean_test_score'])\n",
    "    \n",
    "    return clf.best_params_['n_estimators'], clf.best_params_['max_depth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.52348667 0.59052204 0.59216637 0.59477594 0.59516786 0.59561454\n",
      " 0.59398443 0.5438449  0.60673192 0.60779053 0.61015328 0.60933335\n",
      " 0.61082068 0.61053773 0.56431047 0.62243018 0.62558854 0.62796711\n",
      " 0.63030411 0.63025184 0.63043251 0.57292271 0.63250359 0.64506072\n",
      " 0.64997058 0.65276013 0.65470813 0.65427857 0.59276729 0.69014751\n",
      " 0.7103602  0.71534967 0.72006747 0.72190299 0.72348154]\n",
      "512 7 42\n"
     ]
    }
   ],
   "source": [
    "# extract parameters\n",
    "n_estimator, depth = perform_grid_search(X_train, y_train)\n",
    "c_random_state = 42\n",
    "print(n_estimator, depth, c_random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=7, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=512, n_jobs=None,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Refit a new model with best params, so we can see feature importance\n",
    "rf = RandomForestClassifier(max_depth=depth, n_estimators=n_estimator,\n",
    "                            criterion='entropy', random_state=c_random_state)\n",
    "\n",
    "rf.fit(X_train, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.79      0.78      3261\n",
      "           1       0.79      0.77      0.78      3261\n",
      "\n",
      "   micro avg       0.78      0.78      0.78      6522\n",
      "   macro avg       0.78      0.78      0.78      6522\n",
      "weighted avg       0.78      0.78      0.78      6522\n",
      "\n",
      "Confusion Matrix\n",
      "[[2587  674]\n",
      " [ 759 2502]]\n",
      "\n",
      "Accuracy\n",
      "0.7802821220484514\n"
     ]
    },
    {
     "data": {
      "image/png": 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7DvHYx8v4Yc2uo7bHVS5NVKTQO7EmQ7s2pEJpaxUY30lJSWHMmDFcd911jB07lkqVKjkdkl9ZUjCOeurTFfywZhfZubms3XnIs/38lrW4vkM8repWtPEB43MHDx5k1qxZXHvttbRo0YJVq1aFbekcSwrGERlZOZw5/EuOZLsmlKlUOpqkepUY2q0hPZpVD/l+WxM45s6dy+DBg9m8eTNJSUkkJCSEbUIASwrGzzKychj65iK+yzdOsPDRXlQtW9LBqEw42r17N/fccw9vvPEGzZo144cffgjKAnbFzZKC8atxX6/2JIQhXRtyb+8mREVa95Dxr7wCdikpKTzyyCM8+uijQVvArrhZUjB+cSAji6FvLvaUnVg5oi+lSthNZca/du7cSZUqVYiMjGT06NHUq1ePVq1aOR1WQLGvaMan9hzK5NLJP9Fy2JeehPD6jWdbQjB+paq89tprNGnShJdffhmAiy++2BJCAaylYIpNbq6yJz2T9xdu5ru/dlC6RBT/W/3P2EHf5jWZfHVrm5nM+NWGDRsYPHgwX331FZ07d6Z79+5OhxTQLCmY03Y4M4f7/7OUT/7YetT2iqWjOTO2ArUqlLJkYBwxc+ZMhg4diogwefJkbrnllpArYFfcLCmYU7J5TzpT563lxzW72JCvImmvhBq0b1iFAUmxlLfSE8ZhNWrUoEuXLkyZMoW4uDinwwkKosdOQRXgkpKSdOHChU6HEdYys3Np8ujnAFQtW4KG1cqSFF+Ju3vZlUTGWVlZWTz77LPk5OTw+OOPOx1OQBGRRaqaVNR+1lIwXkvdm07q3sNc/cqvADStUY65d4fWBCMmeC1evJibbrqJP/74g6uuuspTwM6cHEsK5oSWbUljxKcrWLB+z1Hbq5YtyZw7OzsUlTH/OHz4MMOHD2fMmDFUq1aNWbNmhczUmE7waVIQkb7AC0Ak8Iqqjjrm+ThgBlDRvc+DqjrHlzGZoq3deZAd+48w/JPl/LX9AAD1q5ahSY2y9Glek0bVy9KyTgX7FmYCwrp163j++ee54YYbeO6558KugF1x81lSEJFIYBJwLpAK/CYis1V1Rb7dHgXeV9WXRCQRmAPE+yomU7iMrBzGfrmKl39Yf9xzj56fwM2dGzgQlTEF279/Px999BE33HADzZs3Z82aNSE7E5q/+bKl0BZIUdV1ACLyLnAxkD8pKFDevVwBOPqaRuMX0+atZeScvzzr5UpGMfLSlsRXKUPL2AoORmbM8ebMmcOQIUPYsmUL7dq1IyEhwRJCMfJlUqgDbM63ngq0O2afYcCXInI7UAboVdAbichgYDBgl5UVsx5jv2edu2T11e3iuK1HI2pVKOVwVMYcb9euXdx99928+eabJCYm8tNPP1kBOx/wZVIoqMP52OtfrwReV9WxItIemCkiLVQ196gXqU4DpoHrklSfRBtmNu9J55aZizwJ4f1b2tO2fmWHozKmYHkF7NatW8fjjz/Oww8/TMmSVlnXF3yZFFKBuvnWYzm+eygZ6Augqr+ISAxQFdjhw7jCWkZWDhe9+COr/z7o2fb9vd2Ir1rGwaiMKdjff/9NtWrViIyMZMyYMdSrV48zzjjD6bBCmi/vNPoNaCwi9UWkBDAQmH3MPpuAngAikgDEADsxxS43V3n842U0e+wLT0K45pw41jzdzxKCCTiqyvTp02natCnTpk0D4MILL7SE4Ac+aymoaraI3AbMxXW56auqulxERgALVXU28H/AyyJyN66upRs02G6xDhKPz17Gm/M3AfDYBYnc1DHeLik1AWndunUMGjSIb7/9lq5du9KrV4FDjcZHfHqfgvuegznHbHs83/IKoKMvYwh3GVk5TPhmjSch/PF4b5v43gSsGTNmcOuttxIZGcmUKVMYNGiQFbDzM7ujOYQ9+J+lvPvbPxeATb22jSUEE9Bq165Njx49eOmll4iNjXU6nLBkSSFELdm8z5MQbuveiOs7xFOtnF2tYQJLZmYmo0aNIjc3l2HDhnHuuedy7rnnOh1WWLOkEIK+Wfk3yTNclWSnXNOavi1qORyRMcf77bffuOmmm1i2bBnXXnutFbALENZZF2L+2LzPkxAubV3HEoIJOOnp6dx7772cc8457N27l9mzZ/PGG29YQggQ1lIIEWnpWTw8608++3MbAGMHnMllbaxP1gSe9evXM3HiRAYNGsTo0aOpUMFKqQQSSwoh4Jk5K5k6b51n/b4+TS0hmICSlpbGRx99xI033kjz5s1JSUmhbt26Rb/Q+J0lhSC2Lz2TK1/+lZXb9gNwz7lNuKNnY4ejMuZon332Gbfccgvbtm2jffv2NGvWzBJCALOkEITW7jzIs1/8xdzlf3u2/e++btSrYncmm8Cxc+dO7rrrLt5++21atGjBRx99RLNmzZwOyxTBkkIQSTucRZdnvyPtcJZn2x09G3NHj0Y2N7IJKDk5OXTq1In169czfPhwHnzwQUqUKOF0WMYLlhSCxOs/rWfYJ/9MRTEzuS0dG1YlIsKu2DCBY/v27VSvXp3IyEjGjh1LfHw8LVq0cDoscxLs62UQeGfBJk9CuL59PX59uCedG1ezhGACRm5uLlOnTqVJkyZMnToVgAsuuMASQhAqsqUgIqWAu4B6qjpERBoBjVX1c59HF+ayc3Jp9Mg/v+a3B7WjQ8OqDkZkzPFSUlIYNGgQ33//PT169KBPnz5Oh2ROgzcthVdxTZjTyb2+FRjps4gMANvSDh+VED4c0t4Sggk4r732Gi1btmTx4sW8/PLLfP311zRoYPN5BzNvxhQaq+qVIjIAQFXTxW499Kn//r6Fu95b4llfOqw35WOskJ0JPHFxcfTp04dJkyZRp04dp8MxxcCbpJDpnhFNAUSkPpDp06jCXF5CuKNHI+7p3dThaIz5x5EjR3jmmWfIzc1lxIgR9OzZk549ezodlilG3nQfPQl8AcSKyAzgO+Bhn0YVpn5O2cWFE38EoHPjqpYQTED59ddfadOmDcOHD2fTpk3YfFihqciWgqp+LiILgQ64xhbuU1WbQ7kY5OQqC9bvYX9GFht2HeKZz/8CoFR0JE9f0tLh6IxxOXToEI899hjjx4+nTp06fPrpp5x//vlOh2V8xJurj75U1d7AxwVsM6fh7Ke/Zs+ho3vi7u7VhDt7WakKEzg2btzI5MmTGTJkCKNGjaJ8+fJOh2R8qNCkICIlgBighoiUw9VKACgPxPkhtpD27V9/exLC7Ns6EhMdSc0KMTagbALCvn37+PDDD7n55ptJTEwkJSXFZkILEydqKfwbuAeoDiznn6SwH5ji47hCUnZOLlPnreO5uas82766uwuNa5RzMCpjjvbxxx8zdOhQduzYQadOnWjWrJklhDBS6ECzqo5T1brAA6oap6p13Y/mqjrejzGGjH4v/HBUQniwXzNLCCZg7Nixg4EDB3LJJZdQrVo15s+fbwXswpA3A83jRaQZkIirOylv+9u+DCzU5OQqa3YcBGDho72oWtbmSzaBIycnh44dO7Jp0yaeeuop7r//fqKjrSszHHkz0Pwo0BtoBswF+gA/ApYUTsJHi1MB6H9WHUsIJmBs3bqVmjVrEhkZyQsvvEB8fDyJiYlOh2Uc5M19ClcA3YFtqnotcCZWXdVrqsr4r1dz34dLAdfVRcY4LTc3l5deeolmzZoxZYpriPC8886zhGC8+nA/rKo5IpLtvgppO2DFTbyQlZNLy2FzycjKBaB1XEXiqpR2OCoT7lavXs2gQYOYN28evXr1ol+/fk6HZAKIN0nhdxGpiKsw3kJcVx8t9mlUIeKDhamehPDXk32JiY50OCIT7qZPn85tt91GTEwMr776KjfccANWyszkd8Kk4C58N0xV9wGTRGQuUF5VLSkUYffBIzw8608AfnyguyUEExDi4+Pp168fkyZNolatWk6HYwLQCZOCqqqIfAq0ca+n+CWqEHDRiz8BrjGE2ErWZWScceTIEZ588kkAnnrqKStgZ4rkzUDzAhFp7fNIQsjmPels2XcYgKHdGjocjQlXP//8M61ateLpp59m27ZtVsDOeMWbpNAJV2JYJSKLReR3EbHuoxP4YJHr8tMXrzqLElE246nxr4MHD3LnnXfSqVMn0tPT+eKLL5g+fbqNHRiveDPQfMmpvrmI9AVeACKBV1R1VAH7XA4MwzVfwx+qetWpHi9QTPhmDQDnt7Q+W+N/mzZtYurUqfz73/9m5MiRlCtnd80b73lzR/PaU3ljEYkEJgHnAqnAbyIyW1VX5NunMfAQ0FFV94pI9VM5ViB54WtXQqhZPsa+mRm/2bt3Lx988AGDBw8mMTGRdevWUbt2bafDMkHIl30bbYEUVV2nqpnAu8DFx+wzCJikqnsBgn2ehjl/bmPc16sBmJnc1uFoTLiYNWsWiYmJ3Hrrraxa5aqtZQnBnCpfJoU6wOZ866nubfk1AZqIyE8iMt/d3XQcERksIgtFZOHOnTt9FO7pyc1Vbn3LNdTyyHkJVujO+Nz27dsZMGAAl156KTVr1mTBggU0bWqz9ZnT41W5ChGJBRqr6nciUhKIUtVDRb2sgG3HXv4QBTQGugGxwA8i0sJ9X8Q/L1KdBkwDSEpKCrhLKFSVbmO+B6BtfGUGdbEbvo1v5eTk0LlzZzZv3szIkSO59957rYCdKRbeFMS7CbgNqAA0BOoBk4FeRbw0Faibbz0W2FrAPvNVNQtYLyKrcCWJ37yKPkBMnbeOTXvSAXjvlnMcjsaEstTUVGrXrk1kZCQTJkygfv36Vt7aFCtvuo/uAM7BVd4CVV2Na+KdovwGNBaR+u5Z3AYCs4/Z57+4iu0hIlVxdSet8y70wDHKPbfywkd72eCy8Ync3FwmTpxIs2bNeOmllwDo16+fJQRT7LxJChnugWLAc1VRkZ98qpqNq4UxF1gJvK+qy0VkhIhc5N5tLrBbRFYA3wH3qerukz0Jp+TkKjfPWAhAizrlrSS28Ym//vqLLl26cMcdd9CpUycuuOACp0MyIcybMYWfROR+IEZEuuOapvNTb95cVecAc47Z9ni+ZcU15ec9XkccQC568UeWb90PwMvXJTkcjQlFr7zyCrfddhulS5dmxowZXHvttdYaNT7lTUvhfuAA8BdwJ/AN8Igvgwp02Tm53PDaAk9CWD68D7UqlHI4KhOKGjZsyIUXXsjKlSu57rrrLCEYn5Oi6qGIyIXAF+7BYMclJSXpwoULHY3h8im/sGDDHgA+vb0TLepUcDQeEzoyMjIYMWIEACNHjnQ4GhNKRGSRqhbZpeFNS+FyIEVEXhORPu4xhbC1Lz3TkxCWDuttCcEUm59++olWrVrxzDPPsHPnTitgZxxRZFJwT8HZBPgEuAlYJyJTfB1YoBr8xiIAnvvXGZSPsevCzek7cOAAt99+O507d+bIkSPMnTuXl19+2bqKjCO8uqNZVY8AHwOv47rU9HIfxhSwdh884mkl/KtNrMPRmFCRmprKK6+8wu23386ff/5J7969nQ7JhLEik4KI9BKRV4C1wDXAG0BNXwcWiNo89TUAT17Swr7FmdOye/duz/0GCQkJrFu3jhdeeIGyZcs6HJkJd960FIYAXwAJqnq1qs7Of99CuBg2ezngqn56eZK1EsypUVU+/PBDEhMTueOOOzwF7GxqTBMovBlT+Jeqfqiqh/0RUCDacSCD13/eAMDUa9tQMiqsx9rNKdq2bRuXXXYZAwYMoG7duixcuNAK2JmAU+jNayLyP1XtKiJ7ObqQneC676yyz6MLEE9/thKAMQPO5My6FR2OxgSjvAJ2W7Zs4dlnn+Xuu+8mKsqrepTG+NWJ/iq7u39W9UcggSx1r6uRZIPL5mRt3ryZOnXqEBkZyaRJk6hfvz5NmjRxOixjClVo95Gq5roXp6tqTv4HMN0/4Tlv0ca9LNq4lxrlra6R8V5OTg4TJkw4qoBdnz59LCGYgOdN+/WM/Cvum9fO9k04gWXW76nc/d4fAFx7Tj2HozHBYuXKlSQnJ/PLL7/Qr18/LrzwQqdDMsZrhbYUROQB93jCGSKyx/3YC+zkmCJ3oSovIVyRVJfbejR2OBoTDKZNm0arVq1YvXo1M2fO5LPPPiMuLs7psIzx2omuPnoWqAaMc/+sBlRV1crLjIWyAAAa/0lEQVSqep8/gnPSn6lpAHRuXJXR/zqjiL2NcWncuDH9+/dnxYoVXHPNNXY/iwk6J+o+aqSqa0RkJtA8b2PeH7mqLvVxbI669wNXK+Gec60P2BTu8OHDDBs2DBFh1KhRdO/ene7duxf9QmMC1ImSwoNAMjCpgOcU6OKTiALA4cwcVv19gOhI4ay4Sk6HYwLUvHnzuPnmm1mzZg1DhgxBVa1lYIJeoUlBVZPdPzv7L5zAcN2rvwJwQ4d4ZwMxAWn//v08+OCDvPTSSzRo0IBvvvmGHj16OB2WMcXCm9pHl4pIOffygyLyvoic6fvQnPH2r5v4bcNeAO62riNTgK1bt/L6669zzz33sHTpUksIJqR4U/tomKoeEJEOwIXAe8BU34bljKycXB6e9ScAr914NqVL2B2nxmXXrl1MnjwZgGbNmrF+/XrGjh1LmTJlHI7MmOLlTVLIcf+8AJisqv8BQvJOrk/+2ArANefE0b1pdYejMYFAVXnvvfdITEzkrrvuYvXq1QDUqFHD4ciM8Q1vksI2EZkEDATmiEgJL18XVH7ftJd73nddcXR3L+s2Mq5uoksuuYSBAwdSr149Fi1aZHckm5DnTf/I5cB5wERV3SsitXFdmRRSBr3hmvf5390bUqVsSDaEzEnIycmhS5cubNmyhTFjxnDnnXdaATsTFor8K1fVgyKyAugmIt2AH1T1c59H5kfrdh5k18FMGlYrw319mjkdjnHQxo0biY2NJTIyksmTJ9OgQQMaNWrkdFjG+I03Vx/dBrwPxLkf74vIrb4OzJ+m/7gegIf6JTgciXFKTk4Ozz//PAkJCZ4Cdr1797aEYMKON+3hwUBbVT0IICIjgZ+Byb4MzF+2p2Xw1q+bAOjezAaXw9GyZctITk5mwYIFXHDBBVxyySVOh2SMY7wZMBYgK996lntbSFi8yXVPwgN9mxEZETKnZbw0ZcoUWrduzbp163j77beZPXs2sbE2b4YJX960FGYC80XkP7iSwSXADJ9G5Uef/bkNgAvOsDlyw0leSYqEhAQGDBjA+PHjqVatmtNhGeM4bwaanxWR74C8chdDVPU334blH1k5uXy21JUU6lYu7XA0xh/S09N5/PHHiYyMZPTo0XTt2pWuXbs6HZYxAcPb+w2OuB+H3T9Dwt3vLQGgSxP7hhgOvv/+e8444wzGjh3LwYMHUdWiX2RMmPHm6qNHgHeAWkAs8LaIPOTrwPxh/ro9AMy4MSwmkgtbaWlp3HLLLZ6S1t9++y2TJk2yiqbGFMCbMYVrgDaqmg4gIk8Di4BnfBmYr+05lMmug0fo2KiKfTiEuG3btvHmm29y7733Mnz4cEqXtq5CYwrjTffRRo5OHlHAOm/eXET6isgqEUkRkULvghaRf4mIikiSN+9bHN5fuBmAXglWwyYU7dy5k4kTJwKuAnYbNmzgueees4RgTBG8SQrpwHIReUVEXgb+BPaJyPMi8nxhLxKRSFwT9PQDEoErRSSxgP3KAXcAv57KCZyqT5e6it8NSKrrz8MaH1NV3n77bRISEvi///s/TwE7u7LIGO940330mfuRZ76X790WSFHVdQAi8i5wMbDimP2exDUf9L1evm+xWLZlP3GVS1O2pNWzCRWbN29m6NChfPbZZ7Rr147p06dbATtjTpI3l6ROP8X3rgNszreeCrTLv4OInAXUVdVPRaTQpCAig3HdWU1cXNwphvOPPYcyAes6CiXZ2dl069aN7du3M27cOG6//XYiIyOdDsuYoOPLr8kFjd56rgEUkQhgHHBDUW+kqtOAaQBJSUmnfR3h8q1pALSoU/5038o4bMOGDdStW5eoqCimTp1KgwYNaNCggdNhGRO0fDkvQiqQv8M+Ftiab70c0AL4XkQ2AOcAs/0x2PzDml0ANKhW1teHMj6SnZ3NmDFjSEhI8MyI1qtXL0sIxpwmr1sKIlJSVU/mxrXfgMYiUh/YgmuSnqvynlTVNKBqvvf/HrhXVReexDFOyferdgDQrGY5Xx/K+MDSpUtJTk5m4cKFXHzxxVx22WVOh2RMyPDm5rW2IvInsMa9fqaITCzqdaqaDdwGzAVWAu+r6nIRGSEiF51m3KdlX3oWtSrEEBNtfc7BZvLkybRp04aNGzfy3nvvMWvWLGrXru10WMaEDG9aChNwzc/8XwBV/UNEunvz5qo6B5hzzLbHC9m3mzfvWRwiI4Sk+Mr+OpwpBnkF7Fq0aMHAgQMZN24cVatWLfqFxpiT4k1SiFDVjcfc9Zvjo3j8YltaBuVj7FLUYHDo0CEeffRRoqKieO655+jSpQtdunRxOixjQpY3A82bRaQtoCISKSJ3Aat9HJfPbN13GIAj2bkOR2KK8s0339CyZUvGjx/PkSNHrICdMX7gTVIYCtyDayrOv3FdJTTUl0H50oL1riJ4vRJslrVAtW/fPm6++WZ69epFVFQU8+bNY8KECVajyhg/8ObmtR24rhwKCYcyswFoXMOuPApUf//9N++++y4PPPAATzzxBKVKlXI6JGPCRpFJwV3v6Lh2u6oO9klEPrb/sCsplLPyFgElLxHceeedNG3alA0bNthAsjEO8Kb76GvgG/fjJ6A6QTzRzu6DrtBLW1IICKrKm2++SWJiIvfffz9r1qwBsIRgjEO86T56L/+6iMwEvvJZRD4WFenKg1YIz3mbNm1iyJAhfP7557Rv357p06fTuHFjp8MyJqydyidjfaBecQfiLzsPHKF0CbtpzWl5Bex27NjBhAkTuPXWW62AnTEBwJsxhb38M6YQAewBCp0wJ9D9tmEPuXZpo2PWrVtHvXr1iIqK4uWXX6Zhw4bEx8c7HZYxxu2EYwriugbwTKCa+1FJVRuo6vv+CK645eYqm/akk1jLqqP6W3Z2NqNHjyYxMZFJkyYB0LNnT0sIxgSYE7YUVFVFZJaqtvFXQL40f/1uAJrWtKTgT0uWLCE5OZnFixfTv39/BgwY4HRIxphCeHP10QIRae3zSPzg9037AEjuFO9sIGHkxRdf5Oyzz2bLli18+OGHfPTRR9SqVcvpsIwxhSi0pSAiUe5Kp52AQSKyFjiEa/IcVdWgSxR5N8TGVrLJ230tr4DdGWecwdVXX83zzz9P5cpWhNCYQHei7qMFQGvgEj/F4nMrtx0AoGSUL+cWCm8HDx7kkUceITo6mjFjxlgBO2OCzIk+HQVAVdcW9PBTfMUqwt1SsBo6vvHll1/SokULJk6cSFZWlhWwMyYInailUE1E7insSVV93gfx+NTCDXtJsCuPit3evXu55557eP3112natCnz5s2jU6dOTodljDkFJ2opRAJlcc2lXNAj6Gxxl802xWvHjh18+OGHPPTQQyxZssQSgjFB7EQthW2qOsJvkfhYTq6rK6NhtTIORxIatm/fzjvvvMPdd9/tKWBXpUoVp8MyxpymIscUQkVGlmuyuDNiKzgcSXBTVWbMmEFiYiIPPfSQp4CdJQRjQsOJkkJPv0XhB3kzruXa2Ocp27BhA3379uWGG24gMTGRJUuWWAE7Y0JMod1HqrrHn4H42v4M1zwK9ata99GpyM7Opnv37uzatYtJkyYxZMgQIiLs0l5jQk3Y1I9e/bfrHoVyMWFzysUiJSWF+vXrExUVxauvvkqDBg2oVy9oi+QaY4oQNl/1SrjnUahdwaZ29EZWVhYjR46kefPmngJ23bt3t4RgTIgLm6/NntrfduNakRYvXkxycjJLlixhwIABXHHFFU6HZIzxk7BpKeSxnHBiEyZMoG3btmzfvp2PPvqI999/nxo1ajgdljHGT8ImKVjJhRPL+/2cddZZXHfddaxYsYL+/fs7HJUxxt/CpvvIFOzAgQM89NBDlCxZkrFjx9K5c2c6d+7sdFjGGIeETUvBHO+LL76gRYsWTJ48GVW11pQxJnySgn3c/WP37t1cf/319OvXjzJlyvDTTz/x/PPPW/VYY0z4JIU89rnnSgqzZs3iscce4/fff6d9+/ZOh2SMCRA+TQoi0ldEVolIiog8WMDz94jIChFZKiLfiIjvLoIP86bCtm3bGDNmDKpKkyZN2LhxIyNGjKBkyZJOh2aMCSA+SwoiEglMAvoBicCVIpJ4zG6/A0mqegbwIfCsr+LJF5evDxFQVJVXX32VhIQEHnvsMVJSUgCoVKmSw5EZYwKRL1sKbYEUVV2nqpnAu8DF+XdQ1e9UNd29Oh+I9VUwGoZNhfXr19O7d2+Sk5M588wz+eOPP6yAnTHmhHx5SWodYHO+9VSg3Qn2TwY+L+gJERkMDAaIi4s7raDCpZ2QnZ1Njx492L17Ny+99BKDBw+2AnbGmCL5MikU9Plb4Nd1EbkGSAK6FvS8qk4DpgEkJSWd0lf+cLnacs2aNTRo0ICoqChee+01GjZsSN26dZ0OyxgTJHz51TEVyP9pFAtsPXYnEekFPAJcpKpHfBiP+3i+PoIzsrKyeOqpp2jRogUvvvgiAN26dbOEYIw5Kb5sKfwGNBaR+sAWYCBwVf4dROQsYCrQV1V3+DCWkB5RWLhwIcnJySxdupSBAwdy5ZVXOh2SMSZI+ayloKrZwG3AXGAl8L6qLheRESJykXu354CywAciskREZvsqnjwSYqMKL7zwAu3atWPXrl18/PHHvPPOO1SvXt3psIwxQcqntY9UdQ4w55htj+db7uXL4x99XH8dyT9UFREhKSmJ5ORknn32WSpWrOh0WMaYIBd2BfGCfUxh//79PPDAA8TExDBu3Dg6duxIx44dnQ7LGBMi7BrFIDJnzhyaN2/OtGnTiIqKsgJ2xphiFzZJIZhvXtu1axfXXHMN559/PhUqVODnn3/mueeeC7u7s40xvhc2SSFPMH6M7t27l08++YQnnniCxYsX067die4BNMaYUxc2YwrB1tOyZcsW3nrrLe677z4aN27Mxo0bbSDZGONzYddSCPSmgqry8ssvk5iYyLBhw1i7di2AJQRjjF+ETVIIhobC2rVr6dmzJ4MHD6Z169YsXbqURo0aOR2WMSaMhE33UZ5AvXktOzubnj17smfPHqZOncrNN99sBeyMMX4XPkkhQAcVVq1aRcOGDYmKimLGjBk0bNiQ2FifVRA3xpgTCruvooFyFWdmZibDhw+nZcuWTJo0CYCuXbtaQjDGOCpsWgqB1E5YsGABycnJLFu2jKuuuoqrr77a6ZCMMQYIx5aCw8cfP3487du399x78NZbb1G1alWHozLGGJewSQpODynklaRo27YtgwYNYvny5VxwwQXOBmWMMccIm+6jPP4uDZGWlsb9999PqVKlGD9+PB06dKBDhw5+jcEYY7wVNi0FJ3zyySckJibyyiuvULJkSStgZ4wJeGGTFPz5gbxz506uuuoqLrroIqpUqcL8+fMZPXq0FbAzxgS8sEkKefzxsZyWlsacOXMYPnw4Cxcu5Oyzz/bDUY0x5vSFzZiCr9sJmzdv5s033+TBBx+kUaNGbNy4kQoVKvj4qMYYU7zCr6VQzE2F3NxcpkyZQvPmzXnqqac8BewsIRhjglHYJAVfDCmsWbOGHj16MHToUNq2bcuff/5pBeyMMUEtbLqP8hRXQbzs7GzOPfdc9u3bx/Tp07nxxhttINkYE/TCJikUV0Nh5cqVNG7cmKioKGbOnEnDhg2pXbt2Mb27McY4K2y6jzxO8cv8kSNHeOKJJzjjjDN48cUXAejcubMlBGNMSAmflsJpDCrMnz+f5ORkVqxYwbXXXsu1115bjJEZY0zgCLuWwsl2+48dO5YOHTpw4MAB5syZwxtvvEGVKlV8E5wxxjgs7JKCt3JzcwFo3749Q4YMYdmyZfTr18/hqIwxxrfCpvsoT1ENhX379vF///d/lC5dmokTJ1oBO2NMWLGWQj7//e9/SUxMZMaMGZQrV84K2Bljwk7YJIUTfb7v2LGDyy+/nP79+1OjRg0WLFjAyJEj7b4DY0zYCZukkKegD/r9+/fz1Vdf8fTTT7NgwQJat27tQGTGGOO8sBlT0GNuX9u0aRMzZ87k4YcfplGjRmzatIly5co5FJ0xxgQGn7YURKSviKwSkRQRebCA50uKyHvu538VkXhfxgOgublMnjyZ5s2bM3LkSE8BO0sIxhjjw6QgIpHAJKAfkAhcKSKJx+yWDOxV1UbAOGC0r+LJG1Po168f//73v2nfvj3Lly+3AnbGGJOPL1sKbYEUVV2nqpnAu8DFx+xzMTDDvfwh0FN8NLqbd9/B8uXLee2115g7dy7x8fG+OJQxxgQtX44p1AE251tPBdoVto+qZotIGlAF2JV/JxEZDAwGiIuLO6VgGtUoT9ta0Ty7eBHxdeuc0nsYY0yo82VSKOgb/7EXhnqzD6o6DZgGkJSUdEo3D5ybWINzE3ufykuNMSZs+LL7KBWom289Ftha2D4iEgVUAPb4MCZjjDEn4Muk8BvQWETqi0gJYCAw+5h9ZgPXu5f/BXyrdhuxMcY4xmfdR+4xgtuAuUAk8KqqLheREcBCVZ0NTAdmikgKrhbCQF/FY4wxpmg+vXlNVecAc47Z9ni+5QxggC9jMMYY472wK3NhjDGmcJYUjDHGeFhSMMYY42FJwRhjjIcE2xWgIrIT2HiKL6/KMXdLhwE75/Bg5xweTuec66lqtaJ2CrqkcDpEZKGqJjkdhz/ZOYcHO+fw4I9ztu4jY4wxHpYUjDHGeIRbUpjmdAAOsHMOD3bO4cHn5xxWYwrGGGNOLNxaCsYYY07AkoIxxhiPkEwKItJXRFaJSIqIPFjA8yVF5D3387+KSLz/oyxeXpzzPSKyQkSWisg3IlLPiTiLU1HnnG+/f4mIikjQX77ozTmLyOXuf+vlIvK2v2Msbl78bceJyHci8rv77/s8J+IsLiLyqojsEJFlhTwvIjLB/ftYKiKtizUAVQ2pB64y3WuBBkAJ4A8g8Zh9bgWmuJcHAu85Hbcfzrk7UNq9PDQcztm9XzlgHjAfSHI6bj/8OzcGfgcquderOx23H855GjDUvZwIbHA67tM85y5Aa2BZIc+fB3yOa+bKc4Bfi/P4odhSaAukqOo6Vc0E3gUuPmafi4EZ7uUPgZ4iUtDUoMGiyHNW1e9UNd29Oh/XTHjBzJt/Z4AngWeBDH8G5yPenPMgYJKq7gVQ1R1+jrG4eXPOCpR3L1fg+Bkeg4qqzuPEM1BeDLyhLvOBiiJSq7iOH4pJoQ6wOd96qntbgfuoajaQBlTxS3S+4c0555eM65tGMCvynEXkLKCuqn7qz8B8yJt/5yZAExH5SUTmi0hfv0XnG96c8zDgGhFJxTV/y+3+Cc0xJ/v//aT4dJIdhxT0jf/Y62692SeYeH0+InINkAR09WlEvnfCcxaRCGAccIO/AvIDb/6do3B1IXXD1Rr8QURaqOo+H8fmK96c85XA66o6VkTa45rNsYWq5vo+PEf49PMrFFsKqUDdfOuxHN+c9OwjIlG4mpwnaq4FOm/OGRHpBTwCXKSqR/wUm68Udc7lgBbA9yKyAVff6+wgH2z29m/7Y1XNUtX1wCpcSSJYeXPOycD7AKr6CxCDq3BcqPLq//upCsWk8BvQWETqi0gJXAPJs4/ZZzZwvXv5X8C36h7BCVJFnrO7K2UqroQQ7P3MUMQ5q2qaqlZV1XhVjcc1jnKRqi50Jtxi4c3f9n9xXVSAiFTF1Z20zq9RFi9vznkT0BNARBJwJYWdfo3Sv2YD17mvQjoHSFPVbcX15iHXfaSq2SJyGzAX15ULr6rqchEZASxU1dnAdFxNzBRcLYSBzkV8+rw85+eAssAH7jH1Tap6kWNBnyYvzzmkeHnOc4HeIrICyAHuU9XdzkV9erw85/8DXhaRu3F1o9wQzF/yROQdXN1/Vd3jJE8A0QCqOgXXuMl5QAqQDtxYrMcP4t+dMcaYYhaK3UfGGGNOkSUFY4wxHpYUjDHGeFhSMMYY42FJwRhjjIclBROwRCRHRJbke8SfYN/4wqpK+puIJInIBPdyNxHpkO+5ISJynR9jaRXsVUONf4XcfQompBxW1VZOB3Gy3DfI5d0k1w04CPzsfm5KcR9PRKLcNbwK0gpXWZM5xX1cE5qspWCCirtF8IOILHY/OhSwT3MRWeBuXSwVkcbu7dfk2z5VRCILeO0GERnt3m+BiDRyb68nrnko8uajiHNvHyAiy0TkDxGZ597WTUQ+dbdshgB3u4/ZWUSGici9IpIgIguOOa+l7uU2IvI/EVkkInMLqoApIq+LyPMi8h0wWkTaisjP4ppT4GcRaeq+A3gEcIX7+FeISBlx1ev/zb1vQZVlTThzuna4PexR2APXHblL3I9Z7m2lgRj3cmNcd7UCxOOuPw9MBK52L5cASgEJwCdAtHv7ZOC6Ao65AXjEvXwd8Kl7+RPgevfyTcB/3ct/AnXcyxXdP7vle90w4N587+9Zd59XA/fyA8CjuO5c/Rmo5t5+Ba67eI+N83XgUyDSvV4eiHIv9wL+416+AXgx3+tGAtfkxQusBso4/W9tj8B5WPeRCWQFdR9FAy+KSCtcSaNJAa/7BXhERGKBj1R1jYj0BNoAv7nLfJQCCqsB9U6+n+Pcy+2BS93LM3HN0QDwE/C6iLwPfHQyJ4eriNvlwChcH/5XAE1xFfL7yh1nJFBYXZsPVDXHvVwBmOFuFSnusggF6A1cJCL3utdjgDhg5UnGbkKUJQUTbO4G/gbOxNX9edzkOar6toj8CpwPzBWRm3GVG56hqg95cQwtZPm4fVR1iIi0cx9riTtZees9XLWoPnK9la4RkZbAclVt78XrD+VbfhL4TlX7u7utvi/kNQJcpqqrTiJOE0ZsTMEEmwrANnXVyr8W1zfpo4hIA2Cdqk7AVVHyDOAb4F8iUt29T2UpfJ7qK/L9/MW9/DP/FE68GvjR/T4NVfVXVX0c2MXRJY0BDuAq430cVV2Lq7XzGK4EAa5S19XENS8AIhItIs0LiTO/CsAW9/INJzj+XOB2cTdDxFU91xgPSwom2EwGrheR+bi6jg4VsM8VwDIRWQI0wzV14QpcffZfugd0vwIKm8KwpLulcSeulgnAHcCN7tde634O4DkR+dN9Oew8XHMI5/cJ0D9voLmAY70HXMM/8wFk4irnPlpE/sA17nDcYHoBngWeEZGfODpRfgck5g0042pRRANL3TE/6cV7mzBiVVKNyUdcE/Ikqeoup2MxxgnWUjDGGONhLQVjjDEe1lIwxhjjYUnBGGOMhyUFY4wxHpYUjDHGeFhSMMYY4/H/lZEnslKjZB4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Performance Metrics\n",
    "y_pred_rf = rf.predict_proba(X_train)[:, 1]\n",
    "y_pred = rf.predict(X_train)\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_train, y_pred_rf)\n",
    "print(classification_report(y_train, y_pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y_train, y_pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_train, y_pred))\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Validation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.64      0.65       597\n",
      "           1       0.42      0.44      0.43       351\n",
      "\n",
      "   micro avg       0.56      0.56      0.56       948\n",
      "   macro avg       0.54      0.54      0.54       948\n",
      "weighted avg       0.57      0.56      0.57       948\n",
      "\n",
      "Confusion Matrix\n",
      "[[381 216]\n",
      " [197 154]]\n",
      "\n",
      "Accuracy\n",
      "0.5643459915611815\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Meta-label\n",
    "# Performance Metrics\n",
    "y_pred_rf = rf.predict_proba(X_validate)[:, 1]\n",
    "y_pred = rf.predict(X_validate)\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_validate, y_pred_rf)\n",
    "print(classification_report(y_validate, y_pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y_validate, y_pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_validate, y_pred))\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-01-20 14:42:03.869000\n",
      "2017-12-04 15:45:10.747000\n"
     ]
    }
   ],
   "source": [
    "print(X_validate.index.min())\n",
    "print(X_validate.index.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       597\n",
      "           1       0.37      1.00      0.54       351\n",
      "\n",
      "   micro avg       0.37      0.37      0.37       948\n",
      "   macro avg       0.19      0.50      0.27       948\n",
      "weighted avg       0.14      0.37      0.20       948\n",
      "\n",
      "Confusion Matrix\n",
      "[[  0 597]\n",
      " [  0 351]]\n",
      "\n",
      "Accuracy\n",
      "0.370253164556962\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# Primary model\n",
    "primary_forecast = pd.DataFrame(labels['bin'])\n",
    "primary_forecast['pred'] = 1\n",
    "primary_forecast.columns = ['actual', 'pred']\n",
    "\n",
    "start = primary_forecast.index.get_loc('2016-01-20 14:42:03.869000')\n",
    "end = primary_forecast.index.get_loc('2017-12-04 15:45:10.747000') + 1\n",
    "\n",
    "subset_prim = primary_forecast[start:end]\n",
    "\n",
    "# Performance Metrics\n",
    "actual = subset_prim['actual']\n",
    "pred = subset_prim['pred']\n",
    "print(classification_report(y_true=actual, y_pred=pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(actual, pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(actual, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Importance\n",
    "title = 'Feature Importance:'\n",
    "figsize = (15, 5)\n",
    "\n",
    "feat_imp = pd.DataFrame({'Importance':rf.feature_importances_})    \n",
    "feat_imp['feature'] = X.columns\n",
    "feat_imp.sort_values(by='Importance', ascending=False, inplace=True)\n",
    "feat_imp = feat_imp\n",
    "\n",
    "feat_imp.sort_values(by='Importance', inplace=True)\n",
    "feat_imp = feat_imp.set_index('feature', drop=True)\n",
    "feat_imp.plot.barh(title=title, figsize=figsize)\n",
    "plt.xlabel('Feature Importance Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Performance Tear Sheets (In-sample)\n",
    "\n",
    "### Without Meta Labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_daily_returns(intraday_returns):\n",
    "    \"\"\"\n",
    "    This changes returns into daily returns that will work using pyfolio. Its not perfect...\n",
    "    \"\"\"\n",
    "    \n",
    "    cum_rets = ((intraday_returns + 1).cumprod())\n",
    "\n",
    "    # Downsample to daily\n",
    "    daily_rets = cum_rets.resample('B').last()\n",
    "\n",
    "    # Forward fill, Percent Change, Drop NaN\n",
    "    daily_rets = daily_rets.ffill().pct_change().dropna()\n",
    "    \n",
    "    return daily_rets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_dates = X_validate.index\n",
    "base_rets = labels.loc[valid_dates, 'ret']\n",
    "primary_model_rets = get_daily_returns(base_rets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set-up the function to extract the KPIs from pyfolio\n",
    "perf_func = pf.timeseries.perf_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2016-01-21</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2017-12-04</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>23</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>-16.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>-29.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>61.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>-0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-60.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>3.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>93.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-7.7%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Save the statistics in a dataframe\n",
    "perf_stats_all = perf_func(returns=primary_model_rets, \n",
    "                           factor_returns=None, \n",
    "                           positions=None,\n",
    "                           transactions=None,\n",
    "                           turnover_denom=\"AGB\")\n",
    "perf_stats_df = pd.DataFrame(data=perf_stats_all, columns=['Primary Model'])\n",
    "\n",
    "pf.show_perf_stats(primary_model_rets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With Meta Labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_returns = labels.loc[valid_dates, 'ret'] * y_pred\n",
    "daily_meta_rets = get_daily_returns(meta_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/empyrical/stats.py:1511: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  np.abs(np.percentile(returns, 5))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2016-01-21</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2017-12-04</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>23</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>-13.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>-24.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>54.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>-0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-50.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>-1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>125.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-6.9%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Save the KPIs in a dataframe\n",
    "perf_stats_all = perf_func(returns=daily_meta_rets, \n",
    "                           factor_returns=None, \n",
    "                           positions=None,\n",
    "                           transactions=None,\n",
    "                           turnover_denom=\"AGB\")\n",
    "\n",
    "perf_stats_df['Meta Model'] = perf_stats_all\n",
    "\n",
    "pf.show_perf_stats(daily_meta_rets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Perform out-of-sample test\n",
    "### Meta Model Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extarct data for out-of-sample (OOS)\n",
    "X_oos = X['2018-01-02':]\n",
    "y_oos = y['2018-01-02':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.65      0.60      1088\n",
      "           1       0.54      0.45      0.49      1010\n",
      "\n",
      "   micro avg       0.55      0.55      0.55      2098\n",
      "   macro avg       0.55      0.55      0.55      2098\n",
      "weighted avg       0.55      0.55      0.55      2098\n",
      "\n",
      "Confusion Matrix\n",
      "[[709 379]\n",
      " [558 452]]\n",
      "\n",
      "Accuracy\n",
      "0.5533841754051477\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Performance Metrics\n",
    "y_pred_rf = rf.predict_proba(X_oos)[:, 1]\n",
    "y_pred = rf.predict(X_oos)\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_oos, y_pred_rf)\n",
    "print(classification_report(y_oos, y_pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y_oos, y_pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_oos, y_pred))\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00      1088\n",
      "           1       0.48      1.00      0.65      1010\n",
      "\n",
      "   micro avg       0.48      0.48      0.48      2098\n",
      "   macro avg       0.24      0.50      0.32      2098\n",
      "weighted avg       0.23      0.48      0.31      2098\n",
      "\n",
      "Confusion Matrix\n",
      "[[   0 1088]\n",
      " [   0 1010]]\n",
      "\n",
      "Accuracy\n",
      "0.48141086749285034\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# Primary model\n",
    "primary_forecast = pd.DataFrame(labels['bin'])\n",
    "primary_forecast['pred'] = 1\n",
    "primary_forecast.columns = ['actual', 'pred']\n",
    "\n",
    "subset_prim = primary_forecast['2018-01-02':]\n",
    "\n",
    "# Performance Metrics\n",
    "actual = subset_prim['actual']\n",
    "pred = subset_prim['pred']\n",
    "print(classification_report(y_true=actual, y_pred=pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(actual, pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(actual, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Primary Model (Test Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2018-01-18</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2019-01-31</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>12</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>310.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>356.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>121.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>5.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-61.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>4.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>48.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-14.4%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_dates = X_oos.index\n",
    "\n",
    "# Downsample to daily\n",
    "prim_rets_test = labels.loc[test_dates, 'ret']\n",
    "daily_rets_prim = get_daily_returns(prim_rets_test)\n",
    "\n",
    "# Save the statistics in a dataframe\n",
    "perf_stats_all = perf_func(returns=daily_rets_prim, \n",
    "                           factor_returns=None, \n",
    "                           positions=None,\n",
    "                           transactions=None,\n",
    "                           turnover_denom=\"AGB\")\n",
    "\n",
    "perf_stats_df['Primary Model OOS'] = perf_stats_all\n",
    "\n",
    "# pf.create_returns_tear_sheet(labels.loc[test_dates, 'ret'], benchmark_rets=None)\n",
    "pf.show_perf_stats(daily_rets_prim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meta Model (Test Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2018-01-18</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2019-01-31</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>12</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>182.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>205.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>68.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-22.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>5.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>5.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>44.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>2.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-8.1%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: \n",
      "The current behaviour of 'Series.argmin' is deprecated, use 'idxmin'\n",
      "instead.\n",
      "The behavior of 'argmin' will be corrected to return the positional\n",
      "minimum in the future. For now, use 'series.values.argmin' or\n",
      "'np.argmin(np.array(values))' to get the position of the minimum\n",
      "row.\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
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       "  <thead>\n",
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       "      <th>Worst drawdown periods</th>\n",
       "      <th>Net drawdown in %</th>\n",
       "      <th>Peak date</th>\n",
       "      <th>Valley date</th>\n",
       "      <th>Recovery date</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.36</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.86</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>2018-10-25</td>\n",
       "      <td>2018-12-19</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.13</td>\n",
       "      <td>2018-03-01</td>\n",
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       "      <td>2018-04-06</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2018-02-12</td>\n",
       "      <td>2018-02-14</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.94</td>\n",
       "      <td>2018-04-24</td>\n",
       "      <td>2018-05-29</td>\n",
       "      <td>2018-10-11</td>\n",
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     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: \n",
      "The current behaviour of 'Series.argmin' is deprecated, use 'idxmin'\n",
      "instead.\n",
      "The behavior of 'argmin' will be corrected to return the positional\n",
      "minimum in the future. For now, use 'series.values.argmin' or\n",
      "'np.argmin(np.array(values))' to get the position of the minimum\n",
      "row.\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
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\n",
      "text/plain": [
       "<Figure size 1008x4752 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meta_returns = labels.loc[test_dates, 'ret'] * y_pred\n",
    "daily_rets_meta = get_daily_returns(meta_returns)\n",
    "\n",
    "# save the KPIs in a dataframe\n",
    "perf_stats_all = perf_func(returns=daily_rets_meta, \n",
    "                           factor_returns=None, \n",
    "                           positions=None,\n",
    "                           transactions=None,\n",
    "                           turnover_denom=\"AGB\")\n",
    "\n",
    "perf_stats_df['Meta Model OOS'] = perf_stats_all\n",
    "\n",
    "pf.create_returns_tear_sheet(daily_rets_meta, benchmark_rets=None)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
